Sequence analysis is one of the major subjects of bioinformatics. Several existing libraries combine the representation of biological sequences with exact and approximate pattern matching as well as alignment algorithms. We present Jstacs, an open source Java library, which focuses on the statistical analysis of biological sequences instead. Jstacs comprises an efficient representation of sequence data and provides implementations of many statistical models with generative and discriminative approaches for parameter learning. Using Jstacs, classifiers can be assessed and compared on test datasets or by cross-validation experiments evaluating several performance measures. Due to its strictly object-oriented design Jstacs is easy to use and readily extensible.

new class de.jstacs.models.ModelFactory with static classes to construct many standard models

de.jstacs.utils.galaxy.GalaxyAdaptor, an adaptor to Galaxy, which allows for creating Galaxy applications using Jstacs ParameterSets, also requires new interface GalaxyConvertible

new package de.jstacs.models.hmm for a variety of hidden Markov models, which can be learned by different learning principles including generative and discriminative learning principles, maximization and sampling methods

new package de.jstacs.sampling that contains general infrastructure for parameter sampling

new class de.jstacs.scoringFunctions.MappingScoringFunction that allows for internal mapping of symbols from the alphabet

new package de.jstacs.classifier.scoringFunctionBases.sampling containing classifiers that sample their parameters by the Metropolis-Hastings algorithm

new interface de.jstacs.scoringFunctions.SamplingScoringFunction for NormalizableScoringFunctions that can be used in Metropolis-Hastings sampling of parameters

bugfix in XMLParser for cases, where the tag of interest also occurrs within other, nested tags